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Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects.

Title: Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects.
Authors: Ito, S; Liu, X; Ishikawa, Y; Conti, DD; Otomo, N; Kote-Jarai, Z; Suetsugu, H; Eeles, RA; Koike, Y; Hikino, K; Yoshino, S; Tomizuka, K; Horikoshi, M; Ito, K; Uchio, Y; Momozawa, Y; Kubo, M; BioBank Japan Project; Kamatani, Y; Matsuda, K; Haiman, CA; Ikegawa, S; Nakagawa, H; Terao, C
Contributors: Kote-Jarai, Zsofia; Eeles, Rosalind
Publisher Information: NATURE PORTFOLIO
Publication Year: 2023
Collection: The Institute of Cancer Research (ICR): Publications Repository
Subject Terms: Humans; Male; Androgens; Binding Sites; Multifactorial Inheritance; Neoplasms; Second Primary; Prostatic Neoplasms; Receptors; Androgen
Subject Geographic: England
Description: Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10-10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.
Document Type: article in journal/newspaper
File Description: Electronic; application/pdf
Language: English
ISSN: 2041-1723
Relation: 4863; Nature Communications, 2023, 14 (1), pp. 4863 -; https://repository.icr.ac.uk/handle/internal/6074
Availability: https://repository.icr.ac.uk/handle/internal/6074
Rights: http://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.255F2A80
Database: BASE